BETA: A Large Benchmark Database Toward SSVEP-BCI Application

Overview

Journal Front Neurosci

Date 2020 Jul 14

PMID 32655358

Citations 33

Authors

Bingchuan Liu

Xiaoshan Huang

Yijun Wang

Xiaogang Chen

Xiaorong Gao

Affiliations

Soon will be listed here.

Abstract

The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases is still limited in this field. Therefore, we present a nchmark database owards BCI pplication (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA are in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend using the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database can be downloaded from the following link http://bci.med.tsinghua.edu.cn/download.html.

Citing Articles

MP: A steady-state visual evoked potential dataset based on multiple paradigms.

Zhao X, Xu S, Geng K, Zhou T, Xu T, Wang Z iScience. 2025; 27(11):111030.

PMID: 39759080 PMC: 11700636. DOI: 10.1016/j.isci.2024.111030.

Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Wei Q, Li C, Wang Y, Gao X Sci Rep. 2025; 15(1):365.

PMID: 39748063 PMC: 11697369. DOI: 10.1038/s41598-024-84534-6.

Short-length SSVEP data extension by a novel generative adversarial networks based framework.

Pan Y, Li N, Zhang Y, Xu P, Yao D Cogn Neurodyn. 2024; 18(5):2925-2945.

PMID: 39555252 PMC: 11564580. DOI: 10.1007/s11571-024-10134-9.

A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.

Tian P, Xu G, Han C, Du C, Li H, Chen R Sci Rep. 2024; 14(1):27872.

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